Learn which algorithm qualifies as a blind search in AI. Understand why depth-first search (DFS) is categorized as a blind search and how it systematically explores problem spaces without heuristics.
Table of Contents
Question
Which one of the following is a blind search
A. Depth-search first
B. Best-first search
C. Depth-first search
D. Best-search-first
Answer
C. Depth-first search
Explanation
Depth-first search is a blind search.
Blind search, also known as uninformed search, refers to algorithms that explore a problem space without any additional information or heuristics about the goal state. These algorithms rely solely on the problem’s structure and systematically explore all possible solutions until they find the goal. Examples of blind search algorithms include Breadth-First Search (BFS), Depth-First Search (DFS), and Uniform-Cost Search.
Characteristics of Blind Searches
- No Heuristics: They do not use domain-specific knowledge or heuristics to guide the search.
- Systematic Exploration: They explore all nodes or paths in a brute-force manner.
- Goal Detection Only: They can only distinguish between goal and non-goal states.
Why DFS is a Blind Search
Depth-First Search (DFS) is classified as a blind search because:
- It uses a backtracking approach to explore as deeply as possible along each branch before backtracking to explore other branches.
- DFS does not use any heuristic information to prioritize which branch to explore next; instead, it relies on a stack-based Last-In-First-Out (LIFO) mechanism to manage nodes.
- It systematically traverses the graph or tree without knowledge of the goal’s location, making it uninformed or “blind” in its approach.
Other Options Explained
A. Depth-search first: This option seems to be a typographical error and does not represent a valid algorithm.
B. Best-first search & D. Best-search-first: These are informed searches that use heuristics to guide the exploration toward the goal efficiently, making them non-blind searches.
Example Use Case of DFS
DFS is commonly used in scenarios where memory efficiency is critical, such as solving mazes or puzzles, where exploring one path at a time can reduce memory usage compared to breadth-first approaches.
By understanding these principles, it becomes clear why DFS fits the definition of a blind search algorithm.
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